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Start by identifying the exact Wikipedia pageviews data you want to collect. Wikipedia provides this information through their REST API. Access the API by navigating to the Wikimedia REST API endpoint for pageviews, typically in the format: `https://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/{project}/{access}/{agent}/{article}/{granularity}/{start}/{end}`. Replace the placeholders with your specific parameters.
Use a programming language like Python to send an HTTP GET request to the Wikimedia API endpoint. Using a library like `requests`, you can fetch the data by executing a command such as `requests.get(url)`, where `url` is the complete API endpoint with parameters. Parse the JSON response to extract the pageview data you need.
Once you have the data, process it to fit the structure required by Typesense. Typesense expects data in JSON format with fields and values suitable for indexing. Ensure each data entry includes necessary fields such as `title`, `views`, and `timestamp`. You may use Python's built-in functions or libraries like `pandas` to organize and clean the data.
Install and set up a Typesense server on your local machine or a server. Follow the official Typesense installation guide to get the server running. You can use Docker for an easy setup by executing: `docker run -p 8108:8108 -v/tmp/typesense-data:/data typesense/typesense:latest --data-dir /data --api-key=your-api-key`.
Before importing data, define a schema for your Typesense collection. This schema outlines the fields and data types your collection will have. Use the Typesense API to create a new collection with a defined schema by sending a POST request to `http://localhost:8108/collections` with a JSON body specifying your schema.
With your collection ready, prepare your processed Wikipedia data for import. Convert your structured data into a JSON array, where each element corresponds to a document to be indexed. Use the Typesense import API endpoint to send a POST request to `http://localhost:8108/collections/{collection-name}/documents/import`, supplying your JSON array in the request body.
Finally, confirm that your data has been successfully imported into Typesense. Use the Typesense API to perform a search query on your new collection and verify that the data entries match those from your Wikipedia pageviews dataset. This can be done by sending a GET request to `http://localhost:8108/collections/{collection-name}/documents/search` with appropriate query parameters.
By following these steps, you can manually move Wikipedia pageview data into Typesense without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Page view statistics is a tool that is entirely available for Wikipedia pages, that helps to see how many people have visited an article during a given time period. Using Wikipedia Pageviews there are some limitations. There are many things which need to be considered before using such statistics to make conclusions about an ongoing discussion. There are also some software limitations and circumstances that may influence them, both from inside and outside Wikipedia. For aggregating per project and per project per country, a Pageview statistics are available.
The Wikipedia Pageviews API provides access to various types of data related to the pageviews of Wikipedia articles. Some of the categories of data that can be accessed through this API are:
1. Pageviews: The API provides access to the number of pageviews for a particular Wikipedia article over a specific time period.
2. Language: The API allows users to filter the data by language, enabling them to retrieve pageviews for articles in a specific language.
3. Device type: The API provides data on the type of device used to access the Wikipedia article, such as desktop, mobile, or tablet.
4. Geographic location: The API allows users to filter the data by geographic location, enabling them to retrieve pageviews for articles in a specific country or region.
5. Time period: The API provides data on pageviews over a specific time period, such as hourly, daily, weekly, or monthly.
6. Referrer: The API provides data on the source of the pageview, such as whether it was from a search engine or a social media platform.
Overall, the Wikipedia Pageviews API provides a wealth of data related to the popularity and usage of Wikipedia articles, which can be used for various research and analytical purposes.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
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